Abstract
Person re-identification (ReID) aims to extract accurate identity representation features. However, during feature extraction, individual samples are inevitably affected by noise (background, occlusions, and model limitations). Considering that features from the same identity follow a normal distribution around identity centers after training, we propose a Training-Free Feature Centralization ReID framework (Pose2ID) by aggregating the same identity features to reduce individual noise and enhance the stability of identity representation, which preserves the feature's original distribution for following strategies such as re-ranking. Specifically, to obtain samples of the same identity, we introduce two components:Identity-Guided Pedestrian Generation: by leveraging identity features to guide the generation process, we obtain high-quality images with diverse poses, ensuring identity consistency even in complex scenarios such as infrared, and this http URL Feature Centralization: it explores each sample's potential positive samples from its neighborhood. Experiments demonstrate that our generative model exhibits strong generalization capabilities and maintains high identity consistency. With the Feature Centralization framework, we achieve impressive performance even with an ImageNet pre-trained model without ReID training, reaching mAP/Rank-1 of 52.81/78.92 on Market1501. Moreover, our method sets new state-of-the-art results across standard, cross-modality, and occluded ReID tasks, showcasing strong adaptability.
Abstract (translated)
人员重新识别（ReID）的目标是从图像或视频中提取准确的身份表示特征。然而，在特征提取过程中，由于背景干扰、遮挡以及模型局限性等因素的影响，个体样本不可避免地会受到噪声的干扰。考虑到经过训练后的同一身份的特征在身份中心附近遵循正态分布，我们提出了一种无需训练的特征集中化ReID框架（Pose2ID），通过聚集同一个人的身份特征来减少个体噪声，并增强身份表示的稳定性，从而保留了用于后续策略如重新排名等操作的原始特征分布。 具体来说，为了获取同一身份的样本，我们引入了两个组件： 1. 身份引导的人形生成：利用身份特征指导生成过程，获得姿态多样的高质量图像，在红外线等多种复杂场景中也保证了身份的一致性。 2. 特征集中化：从每个样本周围的邻居探索潜在的正向样本。 实验表明，我们的生成模型表现出强大的泛化能力和保持高一致性。使用ImageNet预训练且未经过ReID训练的模型，在Market1501数据集上达到了52.81/mAP和78.92/Rank-1的成绩。此外，我们的方法在标准、跨模态以及遮挡ReID任务中均取得了新的最佳性能，显示出了强大的适应性。
URL
https://arxiv.org/abs/2503.00938
